agentleFS
Alessandro114Alessandro114/scala-mcp-server/skills/competitor-intel/SKILL.md

competitor-intel

Skill2 starsChanged 9 days agoMITView on GitHub

Research competitors using SCALA's 249M+ company database — search by industry, compare financials where available, and generate competitive positioning analysis.

9 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use content-strategy.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • AnthropicSkill

    content-strategy

    Analyzes sales data from PayPal and QuickBooks to find top performers and slow movers, layers in seasonality, and produces a prioritized 30-day content brief: what to push, what offers to run, what to hold. Strategic output only — no calendars or assets. Use when the user asks what to post, wants a content plan, asks what's selling, or what to promote this month.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 9 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use $ARGUMENTS if provided.

    line 11
  2. Very specific:

    Call search_companies with the industry NACE code and country

    line 14
  3. Very specific:

    Call lookup_company for each top result

    line 15
  4. Distinctive:

    Use real data only. Never fabricate company information.

    line 27

“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Competitive Intelligence Report

Research competitors using SCALA Score data.

Input

The user provides: company name or industry, country (default IT), number of competitors (default 5). Use $ARGUMENTS if provided.

Steps

  1. Call search_companies with the industry NACE code and country
  2. Call lookup_company for each top result
  3. Optionally call company_report for the most interesting competitor

Output Format

Comparison table: | Company | Revenue | Employees | Health | Key Strength |

Then:

  • Market position analysis
  • Gaps and opportunities
  • Recommended actions

Use real data only. Never fabricate company information.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.